class_docstr: Calculates the trend of a given window of entries of a column over time.
  
  Description:
      Given a list of numbers and a corresponding list of
      datetimes, return a rolling slope of the linear trend
      of values, starting at the row `gap` rows away from the current row and looking backward
      over the specified time window (by `window_length` and `gap`).
  
      Input datetimes should be monotonic.
  
   Args:
      window_length (int, string, optional): Specifies the amount of data included in each window.
          If an integer is provided, it will correspond to a number of rows. For data with a uniform sampling frequency,
          for example of one day, the window_length will correspond to a period of time, in this case,
          7 days for a window_length of 7.
          If a string is provided, it must be one of pandas' offset alias strings ('1D', '1H', etc),
          and it will indicate a length of time that each window should span.
          The list of available offset aliases can be found at
          https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases.
          Defaults to 3.
      gap (int, string, optional): Specifies a gap backwards from each instance before the
          window of usable data begins. If an integer is provided, it will correspond to a number of rows.
          If a string is provided, it must be one of pandas' offset alias strings ('1D', '1H', etc),
          and it will indicate a length of time between a target instance and the beginning of its window.
          Defaults to 1.
      min_periods (int, optional): Minimum number of observations required for performing calculations
          over the window. Can only be as large as window_length when window_length is an integer.
          When window_length is an offset alias string, this limitation does not exist, but care should be taken
          to not choose a min_periods that will always be larger than the number of observations in a window.
          Defaults to 1.
  
  Examples:
      >>> import pandas as pd
      >>> rolling_trend = RollingTrend()
      >>> times = pd.date_range(start="2019-01-01", freq="1D", periods=10)
      >>> rolling_trend(times, [1, 2, 4, 8, 16, 24, 48, 96, 192, 384]).tolist()
      [nan, nan, nan, 1.4999999999999998, 2.9999999999999996, 5.999999999999999, 7.999999999999999, 16.0, 36.0, 72.0]
  
      We can also control the gap before the rolling calculation.
  
      >>> rolling_trend = RollingTrend(gap=0)
      >>> rolling_trend(times, [1, 2, 4, 8, 16, 24, 48, 96, 192, 384]).tolist()
      [nan, nan, 1.4999999999999998, 2.9999999999999996, 5.999999999999999, 7.999999999999999, 16.0, 36.0, 72.0, 144.0]
  
      We can also control the minimum number of periods required for the rolling calculation.
  
      >>> rolling_trend = RollingTrend(window_length=4, min_periods=4, gap=0)
      >>> rolling_trend(times, [1, 2, 4, 8, 16, 24, 48, 96, 192, 384]).tolist()
      [nan, nan, nan, 2.299999999999999, 4.599999999999998, 6.799999999999996, 12.799999999999992, 26.399999999999984, 55.19999999999997, 110.39999999999993]
  
      We can also set the window_length and gap using offset alias strings.
  
      >>> rolling_trend = RollingTrend(window_length="4D", gap="1D")
      >>> rolling_trend(times, [1, 2, 4, 8, 16, 24, 48, 96, 192, 384]).tolist()
      [nan, nan, nan, 1.4999999999999998, 2.299999999999999, 4.599999999999998, 6.799999999999996, 12.799999999999992, 26.399999999999984, 55.19999999999997]
code: def get_function(self):
      def rolling_trend(datetime, numeric):
          x = pd.Series(numeric.values, index=datetime.values)
          return apply_rolling_agg_to_series(
              x,
              calculate_trend,
              self.window_length,
              self.gap,
              self.min_periods,
          )
  
      return rolling_trend
  
file_path: featuretools/featuretools/primitives/standard/transform/time_series/rolling_trend.py
imports: import pandas
  from woodwork.column_schema import ColumnSchema
  from woodwork.logical_types import Datetime, Double
  from featuretools.primitives.base.transform_primitive_base import TransformPrimitive
  from featuretools.primitives.standard.transform.time_series.utils import apply_rolling_agg_to_series
  from featuretools.utils import calculate_trend
label_desc: Use image node_id 2 for calling the RollingTrend obj's underlying member method code with example usage: obj.get_function() and returns: rolling_trend, apply_rolling_agg_to_series